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of numerical methods for PDEs, expertise in High Performance Computing and proficiency in a programming language. Excellent written and verbal communication skills in English, the capability to conceive, execute
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the environment. The research at Metabolomics and exposomics group is focused on the development of novel analytical and computational tools for metabolomics and exposome research, and applying them in
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systems uses data to improve their own performance, understanding, and to make accurate predictions and has a close connection to applications. Project description The research projects on sustainable
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appropriate benchmarks for the target domain, and determining which fusion architectures best balance performance, interpretability, and computational efficiency. The student will collaborate closely with
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Swedish automotive industry stakholders, we will perform computational modeling of perceived safety and comfort zone boundaries based on in-project data collection from drivers. The modeling will be both
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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and computational mathematics. Researchers working at the division Mathematical Statistics have skills that span many different branches of mathematical statistics, including biostochastics
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the High-Performance and Automatic Computing group (HPAC), and jointly supervised by Paolo Bientinesi and Lars Karlsson. HPAC’s webpage: https://hpac.cs.umu.se/ Admission requirements The general admission
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TensorFlow or PyTorch. The selection among the eligible candidates will be based on the following criteria: The applicant’s documented knowledge and ability to perform high quality research within
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to high turbidity and/or high viscosity. An international consortium of leading groups has thus been established to perform a concerted research effort combining experiments and theory/simulations in